Model Categories

We are categorizing the machine learning models based on the type of data and their applications. Here, we have Computer vision, Natural language processing, Time series and Columnar. Let’s go through each one of them -

Computer Vision models

Computer vision models are machine learning models that are specifically designed to process and understand images and videos. They are used in a wide range of applications such as object detection, image classification, image segmentation, facial recognition, and video analysis.

Natural Language Processing models

NLP (Natural Language Processing) models are machine learning models that are specifically designed to process and understand human language. They are used in a wide range of applications such as text classification, language translation, sentiment analysis, and text generation.

Time Series models

Time series models are a class of machine learning models that are specifically designed to analyze and forecast time-based data. They are used in a wide range of applications such as financial forecasting, weather forecasting, and demand forecasting.

Columnar models

Columnar machine learning models are a type of machine learning model that are optimized for working with columnar data, such as data stored in a relational database. These models are designed to handle large amounts of data and can be trained and deployed on distributed systems. Some examples of columnar machine learning models include deep learning models and gradient boosting models. These models are efficient for handling large datasets and can be used for tasks such as image classification, natural language processing and time series forecasting.